Systems and Methods for Automatically Estimating a Translation Time

ABSTRACT

A method for automatically estimating a translation time comprises receiving translation data, determining one or more translation parameters based on the translation data, retrieving one or more pre-determined translation coefficients associated with the one or more translation parameters, and calculating an estimated translation time based on the one more or more translation parameters and the one or more pre-determined translation coefficients. The method may further comprise reporting the estimated translation time to a user, receiving, from the user, a request to perform a translation associated with the translation data, performing the translation, recording an actual translation time, comparing the actual translation time to the estimated translation time, and based on the comparison, revising the one or more pre-determined translation coefficients to improve the estimating of the translation time.

BACKGROUND

1. Field of the Invention

This application relates generally to data processing and, morespecifically, to systems and methods for automatically estimating atranslation time.

2. Description of Prior Art

Pioneered in the 1950s, computer-aided translation has been a rapidlygrowing field. There are two types of machine translation systems:rules-based and statistical. Introduction of statistical methods haslead to better translations, handling differences in linguistictypology, translation of idioms, and the isolation of anomalies.Furthermore, computer-aided translation software allows forcustomization by domain or profession, thereby improving output bylimiting the scope of allowable substitutions. However, computer-aidedtranslation systems are able to translate more accurately when humantranslators supervise the process and update translation dictionaries.This human intervention leads to improved translation quality throughautomation with repeatable processes and reduced manual errors.

Modern language industry has developed following the availability of theInternet. Achievements of the industry include the ability to quicklytranslate long texts into many languages. However, managing translationservices may be a difficult task and require simultaneously controllingcomplex projects with hundreds of translation tasks. An accurateestimate of the time required to perform a translation task is importantfor resource allocations.

SUMMARY OF THE INVENTION

The present technology may automatically estimate the time required toperform a translation by a translation service. To generate theestimate, the system may receive content, such as a web page or otherdigital content, having words in a source language, the content to betranslated into a target language. The systems and methods maydynamically estimate the time required to perform the translation of thecontent from the source language to the target language. The estimate ofthe translation time may be based on factors such as a number of wordsand/or pages that comprise the content, a band percentage, and a typeand/or subject matter of the content. The “band percentage” may be thepercentage of content that is recognized in the source language aspreviously translated. For example, a time estimate will be faster if99% of the content is comprised of previously translated sentencescompared to if only 75% of the sentences have been previouslytranslated.

In an embodiment, a system for automatically estimating a translationtime comprises a communication module to receive translation data and toretrieve one or more pre-determined translation coefficients associatedwith one or more translation parameters. The system also includes aprocessing module to determine the one or more translation parametersbased on the translation data and to calculate an estimated translationtime based on the one more or more translation parameters and the one ormore pre-determined translation coefficients. The system may furthercomprise a reporting module to report the estimated translation time toa user, wherein the communication module is to receive, from the user, arequest to perform a translation associated with the translation data, atranslation module to perform the translation, a time recording moduleto record an actual translation time, a comparing module to compare theactual translation time to the estimated translation time, and acoefficient adjusting module to revise the one or more pre-determinedtranslation coefficients based on the comparison.

In an embodiment, the translation is a computer-aided translation andthe one or more translation parameters include one or more of thefollowing: a base task time, a target language, a category of thetranslation source, a number of words in the translation source, anumber of pages in the translation source, and a band percentage. Theband percentage is a percentage of translation units in the translationsource that are matched in the translation memory. The translation unitsinclude one or more of the following: words, sentences, and phrases. Thetranslation data includes one or more of the following: a source file, atranslation memory, a suggestion dictionary, and a reference file. Avalue of a translation coefficient is based on the historical importanceof a translation parameter associated with the translation coefficientin calculating the estimated translation time.

In embodiments, steps of methods counterpart to the systems describedherein may be stored on a computer readable storage medium having aprogram embodied thereon, with the program executable by a processor ina computing device. In yet further example embodiments, modules,subsystems, or devices can be adapted to perform the recited steps.Other features and example embodiments are described below.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments are illustrated by way of example and not limitationin the figures of the accompanying drawings, in which like referencesindicate similar elements.

FIG. 1 is a block diagram of a network environment within which systemsand methods for automatically estimating a translation time areimplemented, in accordance with an example embodiment.

FIG. 2 is a block diagram of a translation time estimator, in accordancewith an example embodiment.

FIG. 3 is a workflow diagram of a method for automatically estimating atranslation time, in accordance with an example embodiment.

FIG. 4 is a workflow diagram of a method for translating a document, inaccordance with an example embodiment.

FIGS. 5-14 are screen shots of an interface though which the translationmay be performed, in accordance with an example embodiment.

FIG. 15 is a computing system that may be used to implement the methodsfor automatically estimating a translation time, in accordance with anexample embodiment.

DETAILED DESCRIPTION

In one example embodiment, systems and methods for automaticallyestimating a translation time may receive content, such as a web page orother digital content, with words in a source language and translate thecontent into a target language. To generate the time estimate, thesystem may receive content, such as a web page or other digital content,having words in a source language, the content to be translated into atarget language. The systems and methods may dynamically estimate thetime required to perform the translation of the content from the sourcelanguage to the target language. The estimate of the translation timemay be based on factors such as a number of words and/or pages thatcomprise the content, a band percentage, and a type and/or subjectmatter of the content. The “band percentage” is the percentage ofcontent that is recognized in the source language as previouslytranslated. For example, a time estimate will be faster if 99% of thecontent is comprised of previously translated sentences compared to ifonly 75% of the sentences have been previously translated.

The type (e.g., subject matter) of the content may affect the estimationtime; for example, pharmaceutical subject matter may take longer totranslate than travel subject matter. After each translation, analgorithm utilized to estimate the translation time is updated based onthe actual translation time for the combination of words, pages, bandpercentage and subject matter, and the updated algorithm is applied tosubsequent translation jobs to improve the estimate.

For example, an initial translation time algorithm may be expressed as:

Translation time=F1(word count, band percentage, content type)*C,   Eq.1

C may be a coefficient, for example a coefficient used as a weightingvalue that corresponds to the particular combination of word count, bandpercentage, or content type, respectively. Hence, the base time may bebased on the word count, but may be adjusted based on a coefficientassociated with the band percentage and/or by another coefficientassociated with the content type. Values for coefficients may be storedin a database and retrieved based on the type of work. Other algorithmsto determine the translation time may be used as well.

An example workflow of a method for automatically estimating atranslation time may include receiving a content translation request bytranslation service from a user, retrieving translation coefficientvalues based on parameters of the translation request, calculating anestimated time for translation based on the coefficients, and reportingthe estimated time for translation to the user. If the user accepts theestimate, the translation may be performed (by human translator orautomated translation service) and the actual translation time recorded.If the actual translation time is within the estimated translation time,no adjustment is required. If, on the other hand, the actual translationtime is not within the estimated translated time, one or morecoefficients are adjusted to reflect the actual translation time.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one. In this document, the term“or” is used to refer to a nonexclusive “or,” such that “A or B”includes “A but not B,” “B but not A,” and “A and B,” unless otherwiseindicated.

FIG. 1 is a block diagram of a network environment 100 within whichsystems and methods for automatically estimating a translation time maybe implemented, in accordance with an example embodiment. As shown inFIG. 1, the network environment 100 may include a network (e.g., mayinclude one or more of a wide area network, local area network, publicnetwork, private network, intranet, the Internet, or a combination ofthese) 110, a translation application 120, a translation package 122,computer 124 associated with a translator 126, computer 132 associatedwith a user 130, an application server 140, and a translation timeestimator 200. The network 110 may include data processing nodesinterconnected for the purpose of data communication and maycommunicatively couple various modules depicted in FIG. 1.

The systems and methods for automatically estimating a translation timemay be implemented within the web-based network environment 100.Therefore, the user computer 132 may communicate to the applicationserver 140 via the network 110. The translation time estimator 200 ofthe application server 140 may perform the business logic of the generalworkflow steps of the example methods. The application server 140 mayprovide the translation to the human translator 126 and receive thefinished product when ready.

FIG. 2 is a block diagram of a translation time estimator 200, inaccordance with an example embodiment. Alternative embodiments of thetranslation time estimator 200 may comprise more, less, or functionallyequivalent modules. In some example embodiments, the translation timeestimator 200 may comprise a communication module 202, a reportingmodule 204, a processing module 206, a translation module 208, acomparing module 210, and a coefficient (coefficient) adjusting module212.

It will be appreciated by one of ordinary skill that examples of theforegoing modules may be virtual, and instructions said to be executedby a module may, in fact, be retrieved and executed by a processor. Theforegoing modules may also include memory cards, servers, and/orcomputer discs. Although various modules may be configured to performsome or all of the various steps described herein, fewer or more modulesmay be provided and still fall within the scope of various embodiments.

The communication module 202 may, in some embodiments, receivetranslation data from the user 130. Translation parameters may bedetermined based on the translation data. Some translation parametersmay be determined directly from the translation data provided from auser, such as a desired task time, the target language, and a categoryof the translation source. Other translation parameters may bedetermined by processing the translation data, such as a number of wordsin the translation source, and a number of pages in the translationsource, and a band percentage. Once the translation parameters aredetermined, the communication module 202 may retrieve translationcoefficients associated with a set of translation parameters such as,for example, a base task time, a target language, a category of thetranslation source, a number of words in the translation source, anumber of pages in the translation source, and a band percentage. Thetranslation coefficient(s) may be used as coefficients or weightings ina translation time algorithm, and may be generated from previoustranslations performed for the same set of translations. For example,for a translation of medical journal content translated from English toGerman, three previous translations may average ten minutes per page. Asubsequent translation request from English to German of six pages ofmedical journal content may have an estimate of sixty minutes. Theprocessing module 206 may then calculate an estimated translation timebased on the translation parameters and translation coefficientsretrieved by the communication module 202. Once the processing module206 calculates the estimated translation time, the reporting module 204may report the estimated translation time to the user 130. If the user130 accepts the translation estimate, the translation module 208 mayperform the translation.

Upon completion of the translation, the actual translation time may berecorded and the comparing module 210 may compare the actual time to theestimated time of translation. Based on the comparison, the coefficientadjusting module 212 may revise the translation coefficients to improvefuture estimates. Alternatively, an average time per translation page orword for translation jobs having the same parameters of source language,target language, and content subject matter may be updated based on therecent translation results with matching parameters.

Accurate estimates of the translation time are important for assessinghow much time it will take to do this particular translation so that thework can be allocated to an appropriate translator. This is especiallyimportant in the case of multiple simultaneous projects. The approachdescribed herein allows performing an analysis based on the percentageof the content being previously translated, the word count, page count,and other parameters associated with the source file. Once the projectis completed, the actual time to complete the translation may bemanually recorded and compared to the estimated time. For example,estimated and actual values can be placed in two columns next to eachother and the times compared. With time, the estimates can be refined,and eventually the estimates get better, thereby creating a reliable wayof planning the translation work that keeps coming in. Thus, rather thandoing the estimates manually, they are performed automatically.

Besides the word count parameter, other parameters may include pagecount and word count. The word count may be split into different matchbands, with the match band being the percentage of the likelihood of thetranslation being correct. For example, if there is an exact match, thenthe match band is 100%, no intervention by a human translator isrequired, and the translation is nearly instantaneous. If there areminor differences (for example, the match band is 95%), some minorinterventions by a human translator are necessary but still the effortto correct the translation is minimal. As the match band increases, thesituation may get worse and worse to the point where a human translatormay spend less time by translating the source document from scratchinstead of taking suggestions from the translation memory. Thetranslation memory includes a set of words and sentences that have beenpreviously translated. Different match bands are calculated based onwhether a particular sentence has been previously translated. The systemmay associate a different amount time per each word of the sourcedocument in each match band.

Additionally, the parameters may include the particular type of work;for example, if the type of work is legal translation, it might takelonger than it would take for a marketing translation of the same size.A user may use special portals to provide the translation data in orderto receive an estimated time to translate.

FIG. 3 is a workflow diagram of a method 300 for sharing content, inaccordance with an example embodiment. The method 300 may be performedby processing logic that may comprise hardware (e.g., dedicated logic,programmable logic, microcode, etc.), software (such as run on ageneral-purpose computer system or a dedicated machine), or acombination of both. In one example embodiment, at least a portion ofthe processing logic may reside at the translation time estimator 200,as illustrated in FIG. 2.

As shown in FIG. 3, the method 300 may commence at operation 302 withthe communication module 202 of the translation time estimator 200receiving translation data from a user. Based on the translation datareceived from the user, the processing module 206 may determine one ormore translation parameters to be used in calculating the estimated timeto complete the translation at operation 304. At operation 306, theprocessing module 206 may retrieve translation coefficients associatedwith the translation parameters. At operation 308, the processing module206 may calculate an estimated translation time based on the translationparameters and the translation coefficients.

Once the estimated translation time is completed, the reporting module204 may report the estimated translation time to the user at operation310. If the user would like to proceed with the service, the user mayrequest a translation associated with the translation data be performedat operation 312. At operation 314, the translation module 208 mayperform the translation. The actual translation time can be recorded atoperation 316. At operation 318, the comparing module 210 compares theactual translation time to the estimated translation time. Based on thecomparison performed by the comparing module 210, the coefficientadjusting module 212 may revise the translation coefficients atoperation 320.

FIG. 4 is a diagram of a method 400 for translating a document, inaccordance with an example embodiment. The method 400 may be performedby processing logic that may comprise hardware (e.g., dedicated logic,programmable logic, microcode, etc.), software (such as run on ageneral-purpose computer system or a dedicated machine), or acombination of both. In one example embodiment, the processing logicresides at the translation time estimator 200, as illustrated in FIG. 2.

The method 400 may be performed by the various modules discussed abovewith reference to FIG. 2. Each of these modules may comprise processinglogic. The method illustrates a work cycle as a round-trip fromreceiving the assignment to uploading the completed package. The method400 may commence at operation 402 as a task being assigned to atranslator. The translator may log onto the portal at operation 404,accept the task, and receive the package at operation 406. At operation408, the translator may complete the translation. At operation 410, thetranslator may log onto the portal and may then upload the package andcomplete the task at operation 412.

FIG. 5-14 show screenshots of a user interface to a softwarefunctionality that may be utilized to provide translation time estimatesbased on the analysis described above. The user may start by setting updifferent categories identified by a custom field and whichever tasksare used in that category. An estimated task duration may be displayedin hours, in a column against a relevant task. The basis of thiscalculation may change through the cost calculator on the fly and maycompare the actual time against the estimated to help with refiningfuture planning. A user may decide which types of work are relevant forhis or her work and then create a custom field with pick list valuesrelevant to the types of work. In this example, three types of work havebeen created: general translation work, legal translation work, andtechnical documentation. These are all added to a custom field namedProduct Category.

Different categories may mirror the values in the pick list that wereadded to the product category. The general category is a defaultcategory and may be used if none of the others are specified whencreating the project. Conditions may be defined under which each set oftasks duration rules will be used. Referring to the legal category, thevalue “legal” can be added from the pick list presented. The rules maybe set manually. There may be no default values provided so theadministrator needs to agree as to what values to use for each type ofwork and how quickly, for example in seconds, they can be carried outfor each task.

Various parameters that may be utilized to calculate time. Theparameters may include a task type, such as translate, edit, proofread,correct errors, review comments, and task time. The task time is toprovide a base time that can be used for additional time to prepare forwork in addition to the act of translating itself, or as a base value tobe used where the task is not based on the size of the document ornumber of words, for example. Thus, for example, the administrator couldcreate a new rule for a review task where the only value populated is atask time of 1800 seconds and zero in every other column.

Match bands are an average time per word that it would take to completethe task based on a fixed amount of time per word per match value in thefollowing example bands: Context TM, Repetitions, 100%, 95-99%, 85-94%,75-84%, 50-74%, No Match. Once the analysis is complete, the durationcolumn may be populated with an estimate of how long the work isexpected to take, which enables the project manager to plan resourcesaccordingly. Once the work is complete, the project manager can edit therecorded time for the task, allowing a useful feedback loop that can beused to refine the estimated values in the task duration rules forfuture planning. Then the two columns can be compared side by side anduse the values for an instant comparison as well as in more detailedreporting. This may bring up the small dialog box where a revised numberof hours can be manually entered.

FIG. 15 illustrates an example computing system 1500 that may be used toimplement an embodiment of the present invention. System 1500 of FIG. 15may be implemented in the context of the user interface 140, themultipoint real-time conferencing engine 200, the network 110, and thelike. The computing system 1500 of FIG. 15 includes one or moreprocessors 1510 and main memory 1520. Main memory 1520 stores, in part,instructions and data for execution by processor 1510. Main memory 1520can store the executable code when the system 1500 is in operation. Thesystem 1500 of FIG. 15 may further include a mass storage device 1530,portable storage medium drive(s) 1540, output devices 1550, user inputdevices 1560, a display system 1570, and other peripheral devices 1580.

The components shown in FIG. 15 are depicted as being connected via asingle bus 1590. The components may be connected through one or moredata transport means. Processor 1510 and main memory 1520 may beconnected via a local microprocessor bus, and the mass storage device1530, peripheral device(s) 1580, portable storage medium drive 1540, anddisplay system 1570 may be connected via one or more input/output (I/O)buses.

Mass storage device 1530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor 1510. Mass storagedevice 1530 can store the system software for implementing embodimentsof the present invention for purposes of loading that software into mainmemory 1520.

Portable storage medium drive 1540 operates in conjunction with aportable non-volatile storage medium, such as a floppy disk, compactdisk (CD) or digital video disc (DVD), to input and output data and codeto and from the computer system 1500 of FIG. 15. The system software forimplementing embodiments of the present invention may be stored on sucha portable medium and input to the computer system 1500 via the portablestorage medium drive 1540.

User input devices 1560 provide a portion of a user interface. Userinput devices 1560 may include an alphanumeric keypad, such as akeyboard, for inputting alphanumeric and other information, or apointing device, such as a mouse, a trackball, stylus, or cursordirection keys. Additionally, the system 1500 as shown in FIG. 15includes output devices 1550. Suitable output devices include speakers,printers, network interfaces, and monitors.

Display system 1570 may include a liquid crystal display (LCD) or othersuitable display device. Display system 1570 receives textual andgraphical information and processes the information for output to thedisplay device.

Peripherals 1580 may include any type of computer support device to addadditional functionality to the computer system. Peripheral device(s)1580 may include a modem or a router.

The components contained in the computer system 1500 of FIG. 15 arethose typically found in computer systems that may be suitable for usewith embodiments of the present invention and are intended to representa broad category of such computer components that are well known in theart. Thus, the computer system 1500 of FIG. 15 can be a personalcomputer (PC), hand held computing device, telephone, mobile computingdevice, workstation, server, minicomputer, mainframe computer, or anyother computing device. The computer can also include different busconfigurations, networked platforms, multi-processor platforms, and soforth. Various operating systems can be used, including UNIX, Linux,Windows, Macintosh OS, Palm OS, and other suitable operating systems.

Some of the above-described functions may be composed of instructionsthat are stored on storage media (e.g., computer-readable medium). Theinstructions may be retrieved and executed by the processor. Someexamples of storage media are memory devices, tapes, disks, and thelike. The instructions are operational when executed by the processor todirect the processor to operate in accord with the invention. Thoseskilled in the art are familiar with instructions, processor(s), andstorage media.

It is noteworthy that any hardware platform suitable for performing theprocessing described herein is suitable for use with the invention. Theterms “computer-readable storage medium” and “computer-readable storagemedia” as used herein refer to any medium or media that participate inproviding instructions to a CPU for execution. Such media can take manyforms, including, but not limited to, non-volatile media, volatilemedia, and transmission media. Non-volatile media include, for example,optical or magnetic disks, such as a fixed disk. Volatile media includedynamic memory, such as system RAM. Transmission media include coaxialcables, copper wire, and fiber optics, among others, including the wiresthat comprise one embodiment of a bus. Transmission media can also takethe form of acoustic or light waves, such as those generated duringradio frequency (RF) and infrared (IR) data communications. Common formsof computer-readable media include, for example, a floppy disk, aflexible disk, a hard disk, magnetic tape, any other magnetic medium, aCD-ROM disk, DVD, any other optical medium, any other physical mediumwith patterns of marks or holes, a RAM, a PROM, an EPROM, an EEPROM, aFLASHEPROM, any other memory chip or cartridge, a carrier wave, or anyother medium from which a computer can read.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to a CPU for execution. Abus carries the data to the system RAM, from which a CPU retrieves andexecutes the instructions. The instructions received by the system RAMcan optionally be stored on a fixed disk either before or afterexecution by a CPU.

The above description is illustrative and not restrictive. Manyvariations of the invention will become apparent to those of skill inthe art upon review of this disclosure. The scope of the inventionshould, therefore, be determined not with reference to the abovedescription, but instead should be determined with reference to theappended claims along with their full scope of equivalents. While thepresent invention has been described in connection with a series ofembodiments, these descriptions are not intended to limit the scope ofthe invention to the particular forms set forth herein. It will befurther understood that the methods of the invention are not necessarilylimited to the discrete steps or the order of the steps described. Tothe contrary, the present descriptions are intended to cover suchalternatives, modifications, and equivalents as may be included withinthe spirit and scope of the invention as defined by the appended claimsand otherwise appreciated by one of ordinary skill in the art. Oneskilled in the art will recognize that the Internet service may beconfigured to provide Internet access to one or more computing devicesthat are coupled to the Internet service, and that the computing devicesmay include one or more processors, buses, memory devices, displaydevices, I/O devices, and the like. Furthermore, those skilled in theart may appreciate that the Internet service may be coupled to one ormore databases, repositories, servers, and the like, which may beutilized in order to implement any of the embodiments of the inventionas described herein. One skilled in the art will further appreciate thatthe term “content” comprises one or more of web sites, domains, webpages, web addresses, hyperlinks, URLs, text, pictures, and/or media(such as video, audio, and any combination of audio and video) providedor displayed on a web page, and any combination thereof.

While specific embodiments of, and examples for, the system aredescribed above for illustrative purposes, various equivalentmodifications are possible within the scope of the system, as thoseskilled in the relevant art will recognize. For example, while processesor steps are presented in a given order, alternative embodiments mayperform routines having steps in a different order, and some processes,or steps may be deleted, moved, added, subdivided, combined, and/ormodified to provide alternative or sub-combinations. Each of theseprocesses or steps may be implemented in a variety of different ways. Inaddition, while processes or steps are at times shown as being performedin series, these processes or steps may instead be performed inparallel, or may be performed at different times.

From the foregoing, it will be appreciated that specific embodiments ofthe system have been described herein for purposes of illustration, butthat various modifications may be made without deviating from the spiritand scope of the system. Accordingly, the system is not limited exceptas by the appended claims.

1. A method for automatically estimating a translation time, the methodcomprising: receiving translation data including content to betranslated; and determining one or more translation parameters based onthe translation data; and executing a module by a processor, the modulestored in memory, the executed module configured to calculate anestimated translation time for translating the content based on the onemore or more translation parameters.
 2. The method of claim 1, furthercomprising: retrieving one or more pre-determined translationcoefficients associated with the one or more translation parameters, theestimated translation time calculated at least in part based on the oneor more pre-determined translation coefficients.
 3. The method of claim1, further comprising: reporting the estimated translation time to auser; receiving, from the user, a request to perform a translationassociated with the translation data; performing the translation;recording an actual translation time; comparing the actual translationtime to the estimated translation time; and based on the comparison,revising the one or more pre-determined translation coefficients toimprove the estimating of the translation time.
 4. The method of claim1, wherein the one or more translation parameters include one or more ofthe following: a base task time, a target language, a category of atranslation source, a number of words in the translation source, anumber of pages in the translation source, and a match band.
 5. Themethod of claim 4, wherein the match band is a percentage of translationunits in the translation source matched in a translation memory.
 6. Themethod of claim 5, wherein the translation units include one or more ofthe following: words, sentences, and phrases.
 7. The method of claim 1,wherein the translation data includes one or more of the following: asource file, a translation memory, a suggestion dictionary, and areference file.
 8. The method of claim 1, wherein a value of atranslation coefficient is based on historical importance of atranslation parameter associated with the translation coefficient incalculating of the estimated translation time.
 9. A system forautomatically estimating a translation time, the system comprising: acommunication module stored in memory and executed by a processor toreceive translation data and to retrieve one or more pre-determinedtranslation coefficients associated with one or more translationparameters; and a processing module stored in memory and executed by aprocessor to determine the one or more translation parameters based onthe translation data and to calculate an estimated translation timebased on the one more or more translation parameters and the one or morepre-determined translation coefficients.
 10. The system of claim 9,further comprising: a reporting module to report the estimatedtranslation time to a user, wherein the communication module is toreceive, from the user, a request to perform a translation associatedwith the translation data; a translation module to perform thetranslation; a time recording module to record an actual translationtime; a comparing module to compare the actual translation time to theestimated translation time; and a coefficient adjusting module to revisethe one or more pre-determined translation coefficients based on thecomparison.
 11. The system of claim 9, wherein the translation is acomputer-aided translation.
 12. The system of claim 9, wherein the oneor more translation parameters include one or more of the following: abase task time, a target language, a category of the translation source,a number of words in the translation source, a number of pages in thetranslation source, and a band percentage.
 13. The system of claim 12,wherein the band percentage is a percentage of translation units in thetranslation source matched in the translation memory.
 14. The system ofclaim 13, wherein the translation units include one or more of thefollowing: words, sentences, and phrases.
 15. The system of claim 9,wherein the translation data includes one or more of the following: asource file, a translation memory, a suggestion dictionary, and areference file.
 16. The system of claim 9, wherein a value of atranslation coefficient is based on historical importance of atranslation parameter associated with the translation coefficient incalculating of the estimated translation time.
 17. A computer readablestorage medium having embodied thereon a program, the program beingexecutable by a processor to perform a method for translating content,the method comprising: receiving translation data including content tobe translated; determining one or more translation parameters based onthe translation data; and calculating an estimated translation time fortranslating the content based on the one more or more translationparameters.
 18. The computer readable storage medium of claim 17,further comprising: retrieving one or more pre-determined translationcoefficients associated with the one or more translation parameters, theestimated translation time calculated at least in part based on the oneor more pre-determined translation coefficients.
 19. The computerreadable storage medium of claim 17, further comprising: reporting theestimated translation time to a user; receiving, from the user, arequest to perform a translation associated with the translation data;performing the translation; recording an actual translation time;comparing the actual translation time to the estimated translation time;and based on the comparison, revising the one or more pre-determinedtranslation coefficients to improve the estimating of the translationtime.
 20. The computer readable storage medium of claim 17, wherein theone or more translation parameters include one or more of the following:a base task time, a target language, a category of a translation source,a number of words in the translation source, a number of pages in thetranslation source, and a match band